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Jeong YD, Ejima K, Kim KS, Iwanami S, Hart WS, Thompson RN, Jung IH, Iwami S, Ajelli M, Aihara K. A modeling study to define guidelines for antigen screening in schools and workplaces to mitigate COVID-19 outbreaks. COMMUNICATIONS MEDICINE 2025; 5:2. [PMID: 39753869 PMCID: PMC11699287 DOI: 10.1038/s43856-024-00716-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/17/2024] [Indexed: 01/06/2025] Open
Abstract
BACKGROUND In-person interaction offers invaluable benefits to people. To guarantee safe in-person activities during a COVID-19 outbreak, effective identification of infectious individuals is essential. In this study, we aim to analyze the impact of screening with antigen tests in schools and workplaces on identifying COVID-19 infections. METHODS We assess the effectiveness of various screening test strategies with antigen tests in schools and workplaces through quantitative simulations. The primary outcome of our analyses is the proportion of infected individuals identified. The transmission process at the population level is modeled using a deterministic compartmental model. Infected individuals are identified through screening tests or symptom development. The time-varying sensitivity of antigen tests and infectiousness is determined by a viral dynamics model. Screening test strategies are characterized by the screening schedule, sensitivity of antigen tests, screening duration, timing of screening initiation, and available tests per person. RESULTS Here, we show that early and frequent screening is the key to maximizing the effectiveness of the screening program. For example, 44.5% (95% CI: 40.8-47.5) of infected individuals are identified by daily testing, whereas it is only 33.7% (95% CI: 30.5-37.3) when testing is performed at the end of the program duration. If high sensitivity antigen tests (Detection limit: 6.3 × 10 4 copies/mL) are deployed, it reaches 69.3% (95% CI: 66.5-72.5). CONCLUSIONS High sensitivity antigen tests, high frequency screening tests, and immediate initiation of screening tests are important to safely restart educational and economic activities in-person. Our computational framework is useful for assessing screening programs by incorporating situation-specific factors.
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Affiliation(s)
- Yong Dam Jeong
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Keisuke Ejima
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
| | - Kwang Su Kim
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
| | - Shoya Iwanami
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - William S Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | | | - Il Hyo Jung
- Department of Mathematics, Pusan National University, Busan, South Korea
- Finace Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan, South Korea
| | - Shingo Iwami
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan.
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan.
- Science Groove Inc., Fukuoka, Japan.
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics, Indiana University School of Public Health-, Bloomington, IN, USA
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan
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2
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Tsang TK, Sullivan SG, Meng Y, Lai FTT, Fan M, Huang X, Lin Y, Peng L, Zhang C, Yang B, Ainslie KEC, Cowling BJ. Evaluating the impact of extended dosing intervals on mRNA COVID-19 vaccine effectiveness in adolescents. BMC Med 2024; 22:384. [PMID: 39267060 PMCID: PMC11396738 DOI: 10.1186/s12916-024-03597-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 08/29/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Extending the dosing interval of a primary series of mRNA COVID-19 vaccination has been employed to reduce myocarditis risk in adolescents, but previous evaluation of impact on vaccine effectiveness (VE) is limited to risk after second dose. METHODS We quantified the impact of the dosing interval based on case notifications and vaccination uptake in Hong Kong from January to April 2022, based on calendar-time proportional hazards models and matching approaches. RESULTS We estimated that the hazard ratio (HR) and odds ratio (OR) of infections after the second dose for extended (28 days or more) versus regular (21-27 days) dosing intervals ranged from 0.86 to 0.99 from calendar-time proportional hazards models, and from 0.85 to 0.87 from matching approaches, respectively. Adolescents in the extended dosing groups (including those who did not receive a second dose in the study period) had a higher hazard of infection than those with a regular dosing interval during the intra-dose period (HR 1.66; 95% CI 1.07, 2.59; p = 0.02) after the first dose. CONCLUSIONS Implementing an extended dosing interval should consider multiple factors including the degree of myocarditis risk, the degree of protection afforded by each dose, and the extra protection achievable using an extended dosing interval.
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Affiliation(s)
- Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China.
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
| | - Sheena G Sullivan
- School of Clinical Sciences, Monash University, Melbourne, Australia
- Department of Epidemiology, University of California, Los Angeles, USA
| | - Yu Meng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Francisco Tsz Tsun Lai
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Min Fan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaotong Huang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Liping Peng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Chengyao Zhang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
| | - Kylie E C Ainslie
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China.
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
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Cohen CA, Leung NHL, Kaewpreedee P, Lee KWK, Jia JZ, Cheung AWL, Cheng SMS, Mori M, Ip DKM, Poon LLM, Peiris JSM, Cowling BJ, Valkenburg SA. Antibody Fc receptor binding and T cell responses to homologous and heterologous immunization with inactivated or mRNA vaccines against SARS-CoV-2. Nat Commun 2024; 15:7358. [PMID: 39191745 PMCID: PMC11350167 DOI: 10.1038/s41467-024-51427-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 08/07/2024] [Indexed: 08/29/2024] Open
Abstract
Whole virion inactivated vaccine CoronaVac (C) and Spike (S) mRNA BNT162b2 (B) vaccines differ greatly in their ability to elicit neutralizing antibodies but have somewhat comparable effectiveness in protecting from severe COVID-19. We conducted further analyses for a randomized trial (Cobovax study, NCT05057169) of third dose homologous and heterologous booster vaccination, i.e. four interventions CC-C, CC-B, BB-C and BB-B. Here, we assess vaccine immunogenicity beyond neutralizing function, including S and non-S antibodies with Fc receptor (FcR) binding, antibody avidity and T cell specificity to 6 months post-vaccination. Ancestral and Omicron S-specific IgG and FcR binding are significantly higher by BNT162b2 booster than CoronaVac, regardless of first doses. Nucleocapsid (N) antibodies are only increased in homologous boosted CoronaVac participants (CC-C). CoronaVac primed participants have lower baseline S-specific CD4+ IFNγ+ cells, but are significantly increased by either CoronaVac or BNT162b2 boosters. Priming vaccine content defined T cell peptide specificity preference, with S-specific T cells dominating B primed groups and non-S structural peptides contributing more in C primed groups, regardless of booster type. S-specific CD4+ T cell responses, N-specific antibodies, and antibody effector functions via Fc receptor binding may contribute to protection and compensate for less potent neutralizing responses in CoronaVac recipients.
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Affiliation(s)
- Carolyn A Cohen
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
- Takemi Program in International Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Prathanporn Kaewpreedee
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kelly W K Lee
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Janice Zhirong Jia
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Alan W L Cheung
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Samuel M S Cheng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Masashi Mori
- Research Institute for Bioresources and Biotechnology, Ishikawa Prefectural University, Nonoichi, Japan
| | - Dennis K M Ip
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leo L M Poon
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - J S Malik Peiris
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Sophie A Valkenburg
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
- Department of Microbiology and Immunology, Peter Doherty Institute of Infection and Immunity, The University of Melbourne, Melbourne, Australia.
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Chan CP, Lee SS, Kwan TH, Wong SYS, Yeoh EK, Wong NS. Population Behavior Changes Underlying Phasic Shifts of SARS-CoV-2 Exposure Settings Across 3 Omicron Epidemic Waves in Hong Kong: Prospective Cohort Study. JMIR Public Health Surveill 2024; 10:e51498. [PMID: 38896447 PMCID: PMC11222765 DOI: 10.2196/51498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/26/2023] [Accepted: 05/05/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Exposure risk was shown to have affected individual susceptibility and the epidemic spread of COVID-19. The dynamics of risk by and across exposure settings alongside the variations following the implementation of social distancing interventions are understudied. OBJECTIVE This study aims to examine the population's trajectory of exposure risk in different settings and its association with SARS-CoV-2 infection across 3 consecutive Omicron epidemic waves in Hong Kong. METHODS From March to June 2022, invitation letters were posted to 41,132 randomly selected residential addresses for the recruitment of households into a prospective population cohort. Through web-based monthly surveys coupled with email reminders, a representative from each enrolled household self-reported incidents of SARS-CoV-2 infections, COVID-19 vaccination uptake, their activity pattern in the workplace, and daily and social settings in the preceding month. As a proxy of their exposure risk, the reported activity trend in each setting was differentiated into trajectories based on latent class growth analyses. The associations of different trajectories of SARS-CoV-2 infection overall and by Omicron wave (wave 1: February-April; wave 2: May-September; wave 3: October-December) in 2022 were evaluated by using Cox proportional hazards models and Kaplan-Meier analysis. RESULTS In total, 33,501 monthly responses in the observation period of February-December 2022 were collected from 5321 individuals, with 41.7% (2221/5321) being male and a median age of 46 (IQR 34-57) years. Against an expanding COVID-19 vaccination coverage from 81.9% to 95.9% for 2 doses and 20% to 77.7% for 3 doses, the cumulative incidence of SARS-CoV-2 infection escalated from <0.2% to 25.3%, 32.4%, and 43.8% by the end of waves 1, 2, and 3, respectively. Throughout February-December 2022, 52.2% (647/1240) of participants had worked regularly on-site, 28.7% (356/1240) worked remotely, and 19.1% (237/1240) showed an assorted pattern. For daily and social settings, 4 and 5 trajectories were identified, respectively, with 11.5% (142/1240) and 14.6% (181/1240) of the participants gauged to have a high exposure risk. Compared to remote working, working regularly on-site (adjusted hazard ratio [aHR] 1.47, 95% CI 1.19-1.80) and living in a larger household (aHR 1.12, 95% CI 1.06-1.18) were associated with a higher risk of SARS-CoV-2 infection in wave 1. Those from the highest daily exposure risk trajectory (aHR 1.46, 95% CI 1.07-2.00) and the second highest social exposure risk trajectory (aHR 1.52, 95% CI 1.18-1.97) were also at an increased risk of infection in waves 2 and 3, respectively, relative to the lowest risk trajectory. CONCLUSIONS In an infection-naive population, SARS-CoV-2 transmission was predominantly initiated at the workplace, accelerated in the household, and perpetuated in the daily and social environments, as stringent restrictions were scaled down. These patterns highlight the phasic shift of exposure settings, which is important for informing the effective calibration of targeted social distancing measures as an alternative to lockdown.
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Affiliation(s)
- Chin Pok Chan
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Shui Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- S.H. Ho Research Centre for Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Tsz Ho Kwan
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- S.H. Ho Research Centre for Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Samuel Yeung Shan Wong
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Eng-Kiong Yeoh
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ngai Sze Wong
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- S.H. Ho Research Centre for Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
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5
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Tsang TK, Sullivan SG, Meng Y, Lai FTT, Fan M, Huang X, Lin Y, Peng L, Zhang C, Yang B, Ainslie KEC, Cowling BJ. Evaluating the impact of extended dosing intervals on mRNA COVID-19 vaccine effectiveness in adolescents. RESEARCH SQUARE 2024:rs.3.rs-4518813. [PMID: 38947018 PMCID: PMC11213226 DOI: 10.21203/rs.3.rs-4518813/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Extending the dosing interval of a primary series of mRNA COVID-19 vaccination has been employed to reduce myocarditis risk in adolescents, but previous evaluation of impact on vaccine effectiveness (VE) is limited to risk after second dose. Here, we quantified the impact of the dosing interval based on case notifications and vaccination uptake in Hong Kong from January to April 2022. We estimated that the hazard ratio (HR) and odds ratio (OR) of infections after the second dose for extended (28 days or more) versus regular (21-27 days) dosing intervals ranged from 0.86 to 0.99 from calendar-time proportional hazards models, and from 0.85 to 0.87 from matching approaches, respectively. Adolescents in the extended dosing groups (including those who did not receive a second dose in the study period) had a higher hazard of infection than those with a regular dosing interval during the intra-dose period (HR: 1.66; 95% CI: 1.07, 2.59; p = 0.02) after the first dose. Implementing an extended dosing interval should consider multiple factors including the degree of myocarditis risk, the degree of protection afforded by each dose, and the extra protection achievable using an extended dosing interval.
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6
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Zhang Y, Fu Z, Zhang H, Lin K, Song J, Guo J, Zhang Q, Yuan G, Wang H, Fan M, Zhao Y, Sun R, Guo T, Jiang N, Qiu C, Zhang W, Ai J. Proteomic and Cellular Characterization of Omicron Breakthrough Infections and a Third Homologous or Heterologous Boosting Vaccination in a Longitudinal Cohort. Mol Cell Proteomics 2024; 23:100769. [PMID: 38641227 PMCID: PMC11154224 DOI: 10.1016/j.mcpro.2024.100769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/18/2024] [Accepted: 03/23/2024] [Indexed: 04/21/2024] Open
Abstract
The understanding of dynamic plasma proteome features in hybrid immunity and breakthrough infection is limited. A deeper understanding of the immune differences between heterologous and homologous immunization could assist in the future establishment of vaccination strategies. In this study, 40 participants who received a third dose of either a homologous BBIBP-CorV or a heterologous ZF2001 protein subunit vaccine following two doses of inactivated coronavirus disease 2019 vaccines and 12 patients with BA2.2 breakthrough infections were enrolled. Serum samples were collected at days 0, 28, and 180 following the boosting vaccination and breakthrough and then analyzed using neutralizing antibody tests and mass spectrometer-based proteomics. Mass cytometry of peripheral blood mononuclear cell samples was also performed in this cohort. The chemokine signaling pathway and humoral response markers (IgG2 and IgG3) associated with infection were found to be upregulated in breakthrough infections compared to vaccination-induced immunity. Elevated expression of IGKV, IGHV, IL-17 signaling, and the phagocytosis pathway, along with lower expression of FGL2, were correlated with higher antibody levels in the boosting vaccination groups. The MAPK signaling pathway and Fc gamma R-mediated phagocytosis were more enriched in the heterologous immunization groups than in the homologous immunization groups. Breakthrough infections can trigger more intensive inflammatory chemokine responses than vaccination. T-cell and innate immune activation have been shown to be closely related to enhanced antibody levels after vaccination and therefore might be potential targets for vaccine adjuvant design.
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Affiliation(s)
- Yi Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhangfan Fu
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haocheng Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ke Lin
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jieyu Song
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jingxin Guo
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiran Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Guanmin Yuan
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hongyu Wang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Mingxiang Fan
- Tongji Medical School, Tongji University, Shanghai, China
| | - Yuanhan Zhao
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Rui Sun
- iMarker lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Tiannan Guo
- iMarker lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Ning Jiang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chao Qiu
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenhong Zhang
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; Shanghai huashen institute of microbes and infections, Shanghai, China.
| | - Jingwen Ai
- Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
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7
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Zeng Z, Liang J, Lin Z, Guan W, He W, Li Q, Liang L, Pan W, Liu Z, Lu G, Deng X, Lau EHY, Hon C, Yang Z. The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality - Hong Kong SAR, China, 2022. China CDC Wkly 2024; 6:469-477. [PMID: 38854464 PMCID: PMC11154106 DOI: 10.46234/ccdcw2024.092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/19/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demonstrates increased transmissibility compared to earlier strains, contributing to a significant number of fatalities in Hong Kong Special Administrative Region (HKSAR), China. Adequate medical resources and medications are essential in mitigating these deaths. This study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in HKSAR. Methods Vector autoregression (VAR) was employed to analyze data from the Oxford coronavirus disease 2019 (COVID-19) Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in HKSAR. Additionally, a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on mortality. Results In the initial four waves, workplace closures, restrictions on public events, international travel bans, and shielding the elderly significantly influenced pandemic management. Contrarily, during the fifth wave, these measures showed no notable effects. When comparing a situation without extra medical resources or COVID-19 oral medication, there was a 17.7% decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2% reduction with oral medications. Together, they contributed to a 26.6% decline in fatalities. Discussion With the rapid spread of the virus, regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.
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Affiliation(s)
- Zhiqi Zeng
- Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Jingyi Liang
- Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China
| | - Zhijie Lin
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China
| | - Wenda Guan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Wei He
- Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China
| | - Qianying Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Lixi Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Weiqi Pan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Zige Liu
- Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China
| | - Guibin Lu
- Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China
| | - Xiaoyan Deng
- Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou City, Guangdong Province, China
| | - Eric HY Lau
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong SAR, China
| | - Chitin Hon
- Macao Institute of Systems Engineering, Macao University of Science and Technology, Macau SAR, China
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China
- Guangzhou Laboratory, Guangzhou City, Guangdong Province, China
| | - Zifeng Yang
- Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou City, Guangdong Province, China
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China
- Guangzhou Laboratory, Guangzhou City, Guangdong Province, China
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8
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Hedberg P, Parczewski M, Serwin K, Marchetti G, Bai F, Ole Jensen BE, Pereira JP, Drobniewski F, Reschreiter H, Naumovas D, Ceccherini-Silberstein F, Rubio Quintanares GH, Mwau M, Toscano C, König F, Pfeifer N, Zazzi M, Fanti I, Incardona F, Cozzi-Lepri A, Sönnerborg A, Nauclér P. In-hospital mortality during the wild-type, alpha, delta, and omicron SARS-CoV-2 waves: a multinational cohort study in the EuCARE project. THE LANCET REGIONAL HEALTH. EUROPE 2024; 38:100855. [PMID: 38476753 PMCID: PMC10928271 DOI: 10.1016/j.lanepe.2024.100855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/10/2024] [Accepted: 01/19/2024] [Indexed: 03/14/2024]
Abstract
Background Investigating outcomes of hospitalised COVID-19 patients throughout the pandemic is crucial to understand the impact of different SARS-CoV-2 variants. We compared 28-day in-hospital mortality of Wild-type, Alpha, Delta, and Omicron variant infections. Whether the difference in risk by variant varied by age was also evaluated. Methods We conducted a cohort study including patients ≥18 years, hospitalised between 2020 and 02-01 and 2022-10-15 with a SARS-CoV-2 positive test, from nine countries. Variant was classified based on sequenced viruses or from national public metadata. Mortality was compared using the cumulative incidence function and subdistribution hazard ratios (SHR) adjusted for age, sex, calendar time, and comorbidities. Results were shown age-stratified due to effect measure modification (P < 0.0001 for interaction between age and variant). Findings We included 38,585 participants: 19,763 Wild-type, 6387 Alpha, 3640 Delta, and 8795 Omicron. The cumulative incidence of mortality decreased throughout the study period. Among participants ≥70 years, the adjusted SHR (95% confidence interval) for Delta vs. Omicron was 1.66 (1.29-2.13). This estimate was 1.66 (1.17-2.36) for Alpha vs. Omicron, and 1.34 (0.92-1.95) for Wild-type vs. Omicron. These were 1.21 (0.81-1.82), 1.21 (0.68-2.17), and 0.98 (0.53-1.82) among unvaccinated participants. When comparing Omicron sublineages, the aSHR for BA.1 was 1.92 (1.43-2.58) compared to BA.2 and 1.52 (1.11-2.08) compared to BA.5. Interpretation The herein observed decrease in in-hospital mortality seems to reflect a combined effect of immunity from vaccinations and previous infections, although differences in virulence between SARS-CoV-2 variants may also have contributed. Funding European Union's Horizon Europe Research and Innovation Programme.
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Affiliation(s)
- Pontus Hedberg
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Milosz Parczewski
- Department of Tropical Infectious Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Karol Serwin
- Department of Tropical Infectious Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | - Giulia Marchetti
- Department of Health Sciences, Clinic of Infectious Diseases, ASST Santi Paolo E Carlo, University of Milan, Milan, Italy
| | - Francesca Bai
- Department of Health Sciences, Clinic of Infectious Diseases, ASST Santi Paolo E Carlo, University of Milan, Milan, Italy
| | - Björn-Erik Ole Jensen
- Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Duesseldorf, Heinrich Heine University, Duesseldorf, Germany
| | - Joana P.V. Pereira
- Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty and University Hospital Duesseldorf, Heinrich Heine University, Duesseldorf, Germany
| | - Francis Drobniewski
- Department of Infectious Disease, Imperial College, London, W12 0NN, UK
- University Hospital Dorset, Poole Hospital, Poole, Dorset, UK
| | | | - Daniel Naumovas
- Vilnius Santaros Klinikos Biobank, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | - Gibran Horemheb Rubio Quintanares
- Virus Security Department, Paul Ehrlich Institute, Langen, Germany
- Infectious Diseases Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico
| | - Matilu Mwau
- Center for Infectious and Parasitic Diseases Control Research, Kenya Medical Research Institute, Busia, Kenya
| | - Cristina Toscano
- Microbiology Laboratory, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal
| | - Florian König
- Institute for Bioinformatics and Medical Informatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Methods in Medical Informatics Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Nico Pfeifer
- Institute for Bioinformatics and Medical Informatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Methods in Medical Informatics Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Maurizio Zazzi
- Department of Medical Biotechnologies, University of Siena, Italy
| | - Iuri Fanti
- EuResist Network GEIE, Via Guido Guinizelli, 98/100, 00152, Roma, Italy
| | - Francesca Incardona
- EuResist Network GEIE, Via Guido Guinizelli, 98/100, 00152, Roma, Italy
- InformaPRO s.r.l., Via Guido Guinizelli, 98/100, 00152, Roma, Italy
| | - Alessandro Cozzi-Lepri
- Centre for Clinical Research, Epidemiology, Modelling and Evaluation (CREME) Institute for Global Health UCL, Rowland Hill St, London, NW3 2PF, UK
| | - Anders Sönnerborg
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Pontus Nauclér
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Division of Infectious Diseases, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
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9
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Datta S, Vattiato G, Maclaren OJ, Hua N, Sporle A, Plank MJ. The impact of Covid-19 vaccination in Aotearoa New Zealand: A modelling study. Vaccine 2024; 42:1383-1391. [PMID: 38307744 DOI: 10.1016/j.vaccine.2024.01.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/04/2024]
Abstract
Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model of Covid-19 in New Zealand to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among Māori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11%-26% of the 292 Māori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.
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Affiliation(s)
- Samik Datta
- Population Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
| | - Giorgia Vattiato
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand; Manaaki Whenua, Lincoln, New Zealand
| | - Oliver J Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Ning Hua
- Precision Driven Health, Auckland, New Zealand
| | - Andrew Sporle
- Department of Statistics, University of Auckland, Auckland, New Zealand; iNZight Analytics Ltd., Auckland, New Zealand
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
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10
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Lam ICH, Zhang R, Man KKC, Wong CKH, Chui CSL, Lai FTT, Li X, Chan EWY, Lau CS, Wong ICK, Wan EYF. Persistence in risk and effect of COVID-19 vaccination on long-term health consequences after SARS-CoV-2 infection. Nat Commun 2024; 15:1716. [PMID: 38403654 PMCID: PMC10894867 DOI: 10.1038/s41467-024-45953-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/08/2024] [Indexed: 02/27/2024] Open
Abstract
The persisting risk of long-term health consequences of SARS-CoV-2 infection and the protection against such risk conferred by COVID-19 vaccination remains unclear. Here we conducted a retrospective territory-wide cohort study on 1,175,277 patients with SARS-CoV-2 infection stratified by their vaccination status and non-infected controls to evaluate the risk of clinical sequelae, cardiovascular and all-cause mortality using a territory-wide public healthcare database with population-based vaccination records in Hong Kong. A progressive reduction in risk of all-cause mortality was observed over one year between patients with SARS-CoV-2 infection and controls. Patients with complete vaccination or have received booster dose incurred a lower risk of health consequences including major cardiovascular diseases, and all-cause mortality than unvaccinated or patients with incomplete vaccination 30-90 days after infection. Completely vaccinated and patients with booster dose of vaccines did not incur significant higher risk of health consequences from 271 and 91 days of infection onwards, respectively, whilst un-vaccinated and incompletely vaccinated patients continued to incur a greater risk of clinical sequelae for up to a year following SARS-CoV-2 infection. This study provided real-world evidence supporting the effectiveness of COVID-19 vaccines in reducing the risk of long-term health consequences of SARS-CoV-2 infection and its persistence following infection.
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Affiliation(s)
- Ivan Chun Hang Lam
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ran Zhang
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kenneth Keng Cheung Man
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
| | - Carlos King Ho Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Celine Sze Ling Chui
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong SAR, Hong Kong, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
| | - Francisco Tsz Tsun Lai
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
| | - Xue Li
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Esther Wai Yin Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Hong Kong SAR, China
| | - Chak Sing Lau
- Division of Rheumatology and Clinical Immunology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ian Chi Kei Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China.
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China.
- Aston Pharmacy School, Aston University, Birmingham, UK.
| | - Eric Yuk Fai Wan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Sha Tin, Hong Kong SAR, China.
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China.
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11
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Zhang J, Hom K, Zhang C, Nasr M, Gerzanich V, Zhang Y, Tang Q, Xue F, Simard JM, Zhao RY. SARS-CoV-2 ORF3a Protein as a Therapeutic Target against COVID-19 and Long-Term Post-Infection Effects. Pathogens 2024; 13:75. [PMID: 38251382 PMCID: PMC10819734 DOI: 10.3390/pathogens13010075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 has posed unparalleled challenges due to its rapid transmission, ability to mutate, high mortality and morbidity, and enduring health complications. Vaccines have exhibited effectiveness, but their efficacy diminishes over time while new variants continue to emerge. Antiviral medications offer a viable alternative, but their success has been inconsistent. Therefore, there remains an ongoing need to identify innovative antiviral drugs for treating COVID-19 and its post-infection complications. The ORF3a (open reading frame 3a) protein found in SARS-CoV-2, represents a promising target for antiviral treatment due to its multifaceted role in viral pathogenesis, cytokine storms, disease severity, and mortality. ORF3a contributes significantly to viral pathogenesis by facilitating viral assembly and release, essential processes in the viral life cycle, while also suppressing the body's antiviral responses, thus aiding viral replication. ORF3a also has been implicated in triggering excessive inflammation, characterized by NF-κB-mediated cytokine production, ultimately leading to apoptotic cell death and tissue damage in the lungs, kidneys, and the central nervous system. Additionally, ORF3a triggers the activation of the NLRP3 inflammasome, inciting a cytokine storm, which is a major contributor to the severity of the disease and subsequent mortality. As with the spike protein, ORF3a also undergoes mutations, and certain mutant variants correlate with heightened disease severity in COVID-19. These mutations may influence viral replication and host cellular inflammatory responses. While establishing a direct link between ORF3a and mortality is difficult, its involvement in promoting inflammation and exacerbating disease severity likely contributes to higher mortality rates in severe COVID-19 cases. This review offers a comprehensive and detailed exploration of ORF3a's potential as an innovative antiviral drug target. Additionally, we outline potential strategies for discovering and developing ORF3a inhibitor drugs to counteract its harmful effects, alleviate tissue damage, and reduce the severity of COVID-19 and its lingering complications.
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Affiliation(s)
- Jiantao Zhang
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (J.Z.); (C.Z.)
| | - Kellie Hom
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA; (K.H.); (F.X.)
| | - Chenyu Zhang
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (J.Z.); (C.Z.)
| | - Mohamed Nasr
- Drug Development and Clinical Sciences Branch, Division of AIDS, NIAID, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Volodymyr Gerzanich
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (V.G.); (J.M.S.)
| | - Yanjin Zhang
- Department of Veterinary Medicine, University of Maryland, College Park, MD 20742, USA;
| | - Qiyi Tang
- Department of Microbiology, Howard University College of Medicine, Washington, DC 20059, USA;
| | - Fengtian Xue
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA; (K.H.); (F.X.)
| | - J. Marc Simard
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (V.G.); (J.M.S.)
- Research & Development Service, VA Maryland Health Care System, Baltimore, MD 21201, USA
| | - Richard Y. Zhao
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (J.Z.); (C.Z.)
- Research & Development Service, VA Maryland Health Care System, Baltimore, MD 21201, USA
- Department of Microbiology-Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Institute of Global Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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12
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Xu X, Deng Y, Ding J, Zheng X, Wang C, Wang D, Liu L, Gu H, Peiris M, Poon LLM, Zhang T. Wastewater genomic sequencing for SARS-CoV-2 variants surveillance in wastewater-based epidemiology applications. WATER RESEARCH 2023; 244:120444. [PMID: 37579567 DOI: 10.1016/j.watres.2023.120444] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Abstract
Wastewater-based epidemiology (WBE) has been widely used as a complementary approach to SARS-CoV-2 clinical surveillance. Wastewater genomic sequencing could provide valuable information on the genomic diversity of SARS-CoV-2 in the surveyed population. However, reliable detection and quantification of variants or mutations remain challenging. In this study, we used mock wastewater samples created by spiking SARS-CoV-2 variant standard RNA into wastewater RNA to evaluate the impacts of sequencing throughput on various aspects such as genome coverage, mutation detection, and SARS-CoV-2 variant deconvolution. We found that wastewater datasets with sequencing throughput greater than 0.5 Gb yielded reliable results in genomic analysis. In addition, using in silico mock datasets, we evaluated the performance of the adopted pipeline for variant deconvolution. By sequencing 86 wastewater samples covering more than 6 million people over 7 months, we presented two use cases of wastewater genomic sequencing for surveying COVID-19 in Hong Kong in WBE applications, including the replacement of Delta variants by Omicron variants, and the prevalence and development trends of three Omicron sublineages. Importantly, the wastewater genomic sequencing data were able to reveal the variant trends 16 days before the clinical data did. By investigating mutations of the spike (S) gene of the SARS-CoV-2 virus, we also showed the potential of wastewater genomic sequencing in identifying novel mutations and unique alleles. Overall, our study demonstrated the crucial role of wastewater genomic surveillance in providing valuable insights into the emergence and monitoring of new SARS-CoV-2 variants and laid a solid foundation for the development of genomic analysis methodologies for WBE of other novel emerging viruses in the future.
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Affiliation(s)
- Xiaoqing Xu
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yu Deng
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiahui Ding
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiawan Zheng
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Chunxiao Wang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Dou Wang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Lei Liu
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Haogao Gu
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Malik Peiris
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Leo L M Poon
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Tong Zhang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
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13
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Pochtovyi AA, Kustova DD, Siniavin AE, Dolzhikova IV, Shidlovskaya EV, Shpakova OG, Vasilchenko LA, Glavatskaya AA, Kuznetsova NA, Iliukhina AA, Shelkov AY, Grinkevich OM, Komarov AG, Logunov DY, Gushchin VA, Gintsburg AL. In Vitro Efficacy of Antivirals and Monoclonal Antibodies against SARS-CoV-2 Omicron Lineages XBB.1.9.1, XBB.1.9.3, XBB.1.5, XBB.1.16, XBB.2.4, BQ.1.1.45, CH.1.1, and CL.1. Vaccines (Basel) 2023; 11:1533. [PMID: 37896937 PMCID: PMC10611309 DOI: 10.3390/vaccines11101533] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/20/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
The spread of COVID-19 continues, expressed by periodic wave-like increases in morbidity and mortality. The reason for the periodic increases in morbidity is the emergence and spread of novel genetic variants of SARS-CoV-2. A decrease in the efficacy of monoclonal antibodies (mAbs) has been reported, especially against Omicron subvariants. There have been reports of a decrease in the efficacy of specific antiviral drugs as a result of mutations in the genes of non-structural proteins. This indicates the urgent need for practical healthcare to constantly monitor pathogen variability and its effect on the efficacy of preventive and therapeutic drugs. As part of this study, we report the results of the continuous monitoring of COVID-19 in Moscow using genetic and virological methods. As a result of this monitoring, we determined the dominant genetic variants and identified the variants that are most widespread, not only in Moscow, but also in other countries. A collection of viruses from more than 500 SARS-CoV-2 isolates has been obtained and characterized. The genetic lines XBB.1.9.1, XBB.1.9.3, XBB.1.5, XBB.1.16, XBB.2.4, BQ.1.1.45, CH.1.1, and CL.1, representing the greatest concern, were identified among the dominant variants. We studied the in vitro efficacy of mAbs Tixagevimab + Cilgavimab (Evusheld), Sotrovimab, Regdanvimab, Casirivimab + Imdevimab (Ronapreve), and Bebtelovimab, as well as the specific antiviral drugs Remdesivir, Molnupiravir, and Nirmatrelvir, against these genetic lines. At the current stage of the COVID-19 pandemic, the use of mAbs developed against early SARS-CoV-2 variants has little prospect. Specific antiviral drugs retain their activity, but further monitoring is needed to assess the risk of their efficacy being reduced and adjust recommendations for their use.
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Affiliation(s)
- Andrei A. Pochtovyi
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
- Department of Virology, Biological Faculty, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Daria D. Kustova
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
- Department of Virology, Biological Faculty, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrei E. Siniavin
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Inna V. Dolzhikova
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Elena V. Shidlovskaya
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | | | - Lyudmila A. Vasilchenko
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Arina A. Glavatskaya
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Nadezhda A. Kuznetsova
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Anna A. Iliukhina
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Artem Y. Shelkov
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Olesia M. Grinkevich
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | | | - Denis Y. Logunov
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
| | - Vladimir A. Gushchin
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
- Department of Virology, Biological Faculty, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Alexander L. Gintsburg
- Federal State Budget Institution “National Research Centre for Epidemiology and Microbiology Named after Honorary Academician N. F. Gamaleya” of the Ministry of Health of the Russian Federation, 123098 Moscow, Russia; (D.D.K.)
- Department of Infectiology and Virology, Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov, First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119435 Moscow, Russia
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14
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Luo J, Zhang J, Tang HT, Wong HK, Lyu A, Cheung CH, Bian Z. Prevalence and risk factors of long COVID 6-12 months after infection with the Omicron variant among nonhospitalized patients in Hong Kong. J Med Virol 2023; 95:e28862. [PMID: 37334978 DOI: 10.1002/jmv.28862] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/21/2023]
Abstract
Long COVID has been reported among patients with COVID-19, but little is known about the prevalence and risk factors associated with long COVID 6-12 months after infection with the Omicron variant. This is a large-scale retrospective study. A total of 6242 out of 12 950 nonhospitalized subjects of all ages with SARS-CoV-2 infection (confirmed by polymerase chain reaction/rapid antigen test) during the Omicron dominant outbreak (December 31, 2021-May 6, 2022) in Hong Kong were included. Prevalence of long COVID, frequencies of symptoms, and risk factors were analyzed. Three thousand four hundred and thirty (55.0%) subjects reported at least one long COVID symptom. The most reported symptom was fatigue (1241, 36.2%). Female gender, middle age, obesity, comorbidities, vaccination after infection, having more symptoms, and presenting fatigue/chest tightness/headache/diarrhea in the acute stage of illness were identified as associated risk factors for long COVID. Patients who had received three or more doses of vaccine were not associated with a lower risk of long COVID (adjusted odds ratio 1.105, 95% confidence interval 0.985-1.239, p = 0.088). Among patients with at least three doses of vaccine, there was no significant difference in the risk of long COVID between the CoronaVac vaccine and BNT162b2 vaccine (p > 0.05). Omicron infection can lead to long COVID in a significant proportion of nonhospitalized patients 6-12 months after infection. Further investigation is needed to uncover the mechanisms underlying the development of long COVID and determine the impact of various risk factors such as vaccines.
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Affiliation(s)
- Jingyuan Luo
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Centre for Chinese Herbal Medicine Drug Development, Hong Kong Baptist University, Hong Kong SAR, China
| | - Jialing Zhang
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Centre for Chinese Herbal Medicine Drug Development, Hong Kong Baptist University, Hong Kong SAR, China
| | - Hiu To Tang
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Hoi Ki Wong
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Aiping Lyu
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Chun Hoi Cheung
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Zhaoxiang Bian
- Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Centre for Chinese Herbal Medicine Drug Development, Hong Kong Baptist University, Hong Kong SAR, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
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